DifferentialPrivacyServerSideFixedClipping

class DifferentialPrivacyServerSideFixedClipping(strategy: Strategy, noise_multiplier: float, clipping_norm: float, num_sampled_clients: int)[source]

Bases: Strategy

Strategy wrapper for central DP with server-side fixed clipping.

参数:

  • strategy (Strategy) -- The strategy to which DP functionalities will be added by this wrapper.

  • noise_multiplier (float) -- The noise multiplier for the Gaussian mechanism for model updates. A value of 1.0 or higher is recommended for strong privacy.

  • clipping_norm (float) -- The value of the clipping norm.

  • num_sampled_clients (int) -- The number of clients that are sampled on each round.

实例

Create a strategy:

>>> strategy = fl.server.strategy.FedAvg( ... )

Wrap the strategy with the DifferentialPrivacyServerSideFixedClipping wrapper

>>> dp_strategy = DifferentialPrivacyServerSideFixedClipping(
>>>     strategy, cfg.noise_multiplier, cfg.clipping_norm, cfg.num_sampled_clients
>>> )

Methods

aggregate_evaluate(server_round, results, ...)

使用给定的策略汇总评估损失。

aggregate_fit(server_round, results, failures)

Compute the updates, clip, and pass them for aggregation.

configure_evaluate(server_round, parameters, ...)

配置下一轮评估。

configure_fit(server_round, parameters, ...)

配置下一轮训练。

evaluate(server_round, parameters)

使用策略中的评估函数评估模型参数。

initialize_parameters(client_manager)

使用给定的策略初始化全局模型参数。
aggregate_evaluate(server_round: int, results: list[tuple[ClientProxy, EvaluateRes]], failures: list[tuple[ClientProxy, EvaluateRes] | BaseException]) → tuple[float | None, dict[str, bool | bytes | float | int | str]][source]

使用给定的策略汇总评估损失。

aggregate_fit(server_round: int, results: list[tuple[ClientProxy, FitRes]], failures: list[tuple[ClientProxy, FitRes] | BaseException]) → tuple[Parameters | None, dict[str, bool | bytes | float | int | str]][source]

Compute the updates, clip, and pass them for aggregation.

Afterward, add noise to the aggregated parameters.

configure_evaluate(server_round: int, parameters: Parameters, client_manager: ClientManager) → list[tuple[ClientProxy, EvaluateIns]][source]

配置下一轮评估。

configure_fit(server_round: int, parameters: Parameters, client_manager: ClientManager) → list[tuple[ClientProxy, FitIns]][source]

配置下一轮训练。

evaluate(server_round: int, parameters: Parameters) → tuple[float, dict[str, bool | bytes | float | int | str]] | None[source]

使用策略中的评估函数评估模型参数。

initialize_parameters(client_manager: ClientManager) → Parameters | None[source]

使用给定的策略初始化全局模型参数。